Analytics for IOT monitoring and the Manufacturing Industry

Global competition is increasing the pressure on manufacturing in the UK to take advantage of technology to work even smarter and faster. The much talked about Industry 4.0 is a call to action for the manufacturing sector to leverage new technologies.

While improving productivity and efficiency is already top of mind for most businesses, many are still not using data already held in their core operational systems to gain insights into how improvements can be made. This is where a business intelligence dashboard can make a real difference.

The digital transformation of manufacturing will mean that the volume of data collected within core operational systems as well as increasingly from machine sensors, robotics and AI is only set to increase. Business Intelligence solutions and Analytics can help organisations make sense of this data, gain valuable business insights and consolidate data from different solutions to create a single source of data.

The digitally enabled manufacturing operation will be able to use data already collected in core operational systems as well as that from sensors, robotics and AI to analyse data. This will remove the need for manual reporting and spreadsheet manipulation.

Analytics dashboards allow users to visualise data, spot patterns and exceptions in data and easily gain insights. Businesses prepared to move from analytics as operational support and move towards automated decision making using predictive and prescriptive analytics will transform efficiency and productivity.

Predictive Analytics

Our product, Pi Analytics, is a unique self-service analytics tools that allows users to create a model using historical data to predict future outcomes. Trends and characteristics derived from the data are used in the model and can be applied to new data sets to predict future outcomes.

Predictive analytics software can have multiple real-world benefits in the manufacturing arena and self-service analytics means that businesses don’t need data scientists to make sense of data. Examples such as predictive maintenance, real-time condition monitoring and integrated planning and scheduling are just a few examples of the application of predictive analytics.

Self-service analytics tools in the hands of those managing and leading manufacturing operations mean that data insights can be actioned by those best-placed to use them.